Build Model Operations
That Hold Up in Production
Workshops, pipeline coaching, and advisory for engineering teams who want their model work to be repeatable, maintainable, and well understood.
Three Ways We Work With Teams
Each offering is structured to fit into a working team's schedule. We keep things hands-on and practical — no slides without substance.
MLOps Foundations Workshop
Core habits of running models well — versioning, testing, and repeatable builds — taught with practical, copy-ready examples. Tool-agnostic and grounded. Two guided days with sample repositories.
- Starter template included
- Practices checklist provided
- Suited to developers new to operations work
Pipeline Build Coaching
We work alongside a team to shape a reliable training-and-deployment pipeline, explaining choices as we go so the team can maintain it themselves. Three coaching sessions with shared work.
- Reference pipeline provided
- Annotated session notes
- Best for small engineering teams
Team Enablement Advisory
An advisory engagement that helps a development team raise its operational practices steadily — reviewing workflows, tooling, and documentation at a comfortable pace. Three-month engagement with reviews.
- Practices handbook delivered
- Periodic review notes
- Designed for teams scaling model work
What Working Developers Actually Need
MLOps tooling and theory are well-documented. What's harder to find is someone who sits with your team and explains the reasoning, not just the steps.
Tool-Agnostic Approach
We do not push a particular stack. Guidance adapts to what the team already uses, or helps them evaluate options with clear reasoning.
Copy-Ready Materials
Templates, checklists, and reference pipelines are formatted for immediate use. No re-formatting or reverse-engineering required after the session.
Paced for Real Teams
Delivery is structured around the team's existing workload. Sessions do not assume dedicated study time between meetings.
Explained, Not Just Shown
Every recommendation includes the reasoning behind it, so the team can make informed changes later without needing to come back for every decision.
Maintainability First
Pipelines and practices are built to be owned by the team, not dependent on outside support to keep running.
Honest Scope
We scope each engagement to what the team can realistically absorb. There is no padding for hours or features that do not add working value.
Built on NVIDIA and Modern AI Infrastructure
The pipelines and practices we teach are designed for real GPU-accelerated workloads. Our curriculum reflects how AI development actually runs in production — not just how it looks in a tutorial.
NVIDIA GPU Compute
We cover how to structure training runs and inference jobs so teams are not wasting GPU hours. That means proper experiment tracking, reproducible environments, and clear resource scheduling — whether the team is on NVIDIA A100s, L40S instances, or consumer-grade RTX hardware.
NVIDIA NIM & Inference Serving
NVIDIA Inference Microservices (NIM) let teams deploy optimised model containers with predictable latency. We include NIM in our pipeline coaching engagements for teams moving from notebook experiments to serving endpoints that hold up under load.
Container & Runtime Standards
NVIDIA CUDA containers and the NGC catalogue provide a stable base for reproducible AI environments. We build team templates around these so engineers are not debugging driver mismatches between development machines and production servers.
compute
CUDA Accelerated Training
Environment configs, dependency pinning, and multi-GPU job patterns built into starter templates.
inference
TensorRT & NIM Deployment
Optimised model serving pipelines with consistent latency profiles across staging and production.
tracking
Experiment Reproducibility
MLflow or DVC workflows paired with proper GPU metric logging so experiments are actually comparable.
agentic ai
LLM & Agent Pipelines
Operational patterns for teams building on large language models — versioning prompts, managing context, and monitoring outputs in production.
Aligned with NVIDIA's Developer Ecosystem
Our workshop materials reference NVIDIA's published best practices for GPU-accelerated ML workloads, including NGC container usage, CUDA environment management, and NIM deployment patterns. We stay current as the ecosystem evolves.
A Conversation First, No Obligation
If you are not sure which offering fits your team's situation, write to us or call — we are happy to discuss before any commitment is made.
Frequently Asked
Who is the MLOps Foundations Workshop intended for?
The workshop is designed for software developers who are starting to work with machine learning models in production but have not yet built a repeatable operations practice. Prior Python and basic ML knowledge is helpful, but deep MLOps experience is not required.
How are the Pipeline Build Coaching sessions structured?
Three sessions are spread across the engagement, typically one to two weeks apart. Between sessions, the team works on the pipeline with access to shared notes and the reference materials we provide. We review progress at the start of each session and adjust the focus accordingly.
What does the three-month advisory engagement involve week to week?
It varies by team, but typically involves a standing check-in, review of workflows or documentation the team has updated, and notes on observations or recommendations. The pace is deliberately steady — we are not expecting large changes every week. The handbook is built incrementally over the three months.
Are these sessions delivered in person or remotely?
Both options are available. Teams based in Kuala Lumpur can arrange in-person sessions at their office or at our location in Mont Kiara. Remote delivery via video call works well for the coaching and advisory formats, and some clients mix both.
What tools or platforms do you work with?
We are tool-agnostic and will work with whatever the team uses or is evaluating. Common choices include MLflow, DVC, Prefect, Airflow, and cloud-native services on AWS, GCP, or Azure. If the team has no strong preference yet, we can discuss the tradeoffs and help them pick something appropriate for their scale.
How are payments handled, and is there a refund policy?
Payment is in Malaysian Ringgit (MYR) and is arranged by invoice. Workshop payments are collected in advance. Coaching and advisory engagements are invoiced at the start of each billing period. Cancellation and rescheduling terms are outlined in the service agreement provided before engagement starts.
How is the information we share handled?
Any code, architecture details, or internal documentation shared during an engagement is treated as confidential. We do not reference client work in materials or discussions without written agreement. A mutual confidentiality clause can be included in the engagement agreement if preferred.
Our Location
Unit 23-5, Menara 1MK, Kompleks 1 Mont Kiara, 50480 Kuala Lumpur
Get in Touch
Contact Details
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phone +60 3-6203 7918
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email [email protected]
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address Unit 23-5, Menara 1MK,
Kompleks 1 Mont Kiara,
50480 Kuala Lumpur -
working hours Mon – Fri: 9:00 AM – 6:00 PM
Sat: 10:00 AM – 1:00 PM